


"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					42
   __UpperCAmelCase							       =					None







def 				_snake_case      (   __snake_case							:      Any						, __snake_case							:      List[Any]=0.999						, __snake_case							:      Any="cosine"						, ):




   """simple docstring"""







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__snake_case							:      Tuple   ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__snake_case							:      Tuple   ):
         return math.exp(t * -12.0   )

   else:
      raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'   )

   _lowerCamelCase	:					Union[str, Any]   =		[]
   for i in range(a__   ):
      _lowerCamelCase	:					List[str]   =		i / num_diffusion_timesteps
      _lowerCamelCase	:					Optional[int]   =		(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(a__   ) / alpha_bar_fn(a__   )						, a__   )   )
   return torch.tensor(a__						, dtype=torch.floataa   )





class 	lowercase__				(					A_ ,A_				):
   __UpperCAmelCase							       =					1





   @register_to_config
   def __init__(							self      ,   SCREAMING_SNAKE_CASE = 1000      ,   SCREAMING_SNAKE_CASE = 0.00_01      ,   SCREAMING_SNAKE_CASE = 0.02      ,   SCREAMING_SNAKE_CASE = "linear"      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = 0      ,   SCREAMING_SNAKE_CASE = "epsilon"      ,   SCREAMING_SNAKE_CASE = 1.0      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      if kwargs.get("""set_alpha_to_one"""      ,   SCREAMING_SNAKE_CASE) is not None:
         _lowerCamelCase	:					Optional[Any]   =		(
             'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
         )
         deprecate("""set_alpha_to_one"""      ,   """1.0.0"""      ,   SCREAMING_SNAKE_CASE      ,   standard_warn=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Dict   =		kwargs['set_alpha_to_one']
      if trained_betas is not None:
         _lowerCamelCase	:					Any   =		torch.tensor(SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa)
      elif beta_schedule == "linear":
         _lowerCamelCase	:					Union[str, Any]   =		torch.linspace(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa)
      elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         _lowerCamelCase	:					Union[str, Any]   =		(
             torch.linspace(beta_start**0.5      ,   beta_end**0.5      ,   SCREAMING_SNAKE_CASE      ,   dtype=torch.floataa) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCamelCase	:					Optional[Any]   =		betas_for_alpha_bar(SCREAMING_SNAKE_CASE)
      else:
         raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

      _lowerCamelCase	:					Tuple   =		1.0 - self.betas
      _lowerCamelCase	:					Optional[Any]   =		torch.cumprod(self.alphas      ,   dim=0)

      # At every step in inverted ddim, we are looking into the next alphas_cumprod
      # For the final step, there is no next alphas_cumprod, and the index is out of bounds
      # `set_alpha_to_zero` decides whether we set this parameter simply to zero
      # in this case, self.step() just output the predicted noise
      # or whether we use the final alpha of the "non-previous" one.
      _lowerCamelCase	:					str   =		torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

      # standard deviation of the initial noise distribution
      _lowerCamelCase	:					List[Any]   =		1.0

      # setable values
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					int   =		torch.from_numpy(np.arange(0      ,   SCREAMING_SNAKE_CASE).copy().astype(np.intaa))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  torch.FloatTensor:
      return sample





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  int:

      if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
             F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
             F' maximal {self.config.num_train_timesteps} timesteps.')

      _lowerCamelCase	:					Optional[int]   =		num_inference_steps
      _lowerCamelCase	:					Tuple   =		self.config.num_train_timesteps // self.num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # casting to int to avoid issues when num_inference_step is power of 3
      _lowerCamelCase	:					List[str]   =		(np.arange(0      ,   SCREAMING_SNAKE_CASE) * step_ratio).round().copy().astype(np.intaa)
      _lowerCamelCase	:					str   =		torch.from_numpy(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      self.timesteps += self.config.steps_offset





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = 0.0      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   )  ->  Union[DDIMSchedulerOutput, Tuple]:
      # 1. get previous step value (=t+1)
      _lowerCamelCase	:					Tuple   =		timestep + self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      # change original implementation to exactly match noise levels for analogous forward process
      _lowerCamelCase	:					Dict   =		self.alphas_cumprod[timestep]
      _lowerCamelCase	:					Dict   =		(
          self.alphas_cumprod[prev_timestep]
          if prev_timestep < self.config.num_train_timesteps
          else self.final_alpha_cumprod
      )

      _lowerCamelCase	:					Optional[int]   =		1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
         _lowerCamelCase	:					Union[str, Any]   =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         _lowerCamelCase	:					Any   =		model_output
      elif self.config.prediction_type == "sample":
         _lowerCamelCase	:					Optional[int]   =		model_output
         _lowerCamelCase	:					int   =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
         _lowerCamelCase	:					str   =		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         _lowerCamelCase	:					Dict   =		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
         raise ValueError(
             F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
             """ `v_prediction`""")

      # 4. Clip or threshold "predicted x_0"
      if self.config.clip_sample:
         _lowerCamelCase	:					Optional[Any]   =		pred_original_sample.clamp(
             -self.config.clip_sample_range      ,   self.config.clip_sample_range)

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowerCamelCase	:					Union[str, Any]   =		(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowerCamelCase	:					List[str]   =		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
         return (prev_sample, pred_original_sample)
      return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE      ,   pred_original_sample=SCREAMING_SNAKE_CASE)





   def __len__(							self)  ->  Union[str, Any]:
      return self.config.num_train_timesteps









"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	int   )	->       Tuple:
											if dst_width < 0 or dst_height < 0:
																	raise ValueError('Destination width/height should be > 0'   )

											lowerCAmelCase   :List[Any]						=			img
											lowerCAmelCase   :Optional[int]						=			img.shape[1]
											lowerCAmelCase   :Union[str, Any]						=			img.shape[0]
											lowerCAmelCase   :Optional[int]						=			dst_width
											lowerCAmelCase   :Optional[int]						=			dst_height

											lowerCAmelCase   :Any						=			self.src_w / self.dst_w
											lowerCAmelCase   :Dict						=			self.src_h / self.dst_h

											lowerCAmelCase   :int						=			(
											    np.ones((self.dst_h, self.dst_w, 3)     , np.uinta   ) * 255
											)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																							lowerCAmelCase   :Optional[Any]						=			self.img[self.get_y(UpperCAmelCase   )][self.get_x(UpperCAmelCase   )]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_x * x   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_y * y   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   8_00, 6_00
		__SCREAMING_SNAKE_CASE   =   imread('image_data/lena.jpg', 1)
		__SCREAMING_SNAKE_CASE   =   NearestNeighbour(im, dst_w, dst_h)
		n.process()

		imshow(
		    F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
		)
		waitKey(0)
		destroyAllWindows()